
Tensorizing Subgraph Search in the Supernet
Recently, a special kind of graph, i.e., supernet, which allows two node...
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Decoupling Representation and Classifier for Noisy Label Learning
Since convolutional neural networks (ConvNets) can easily memorize noisy...
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A Survey of Labelnoise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
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Graph Neural Network with Automorphic Equivalence Filters
Graph neural network (GNN) has recently been established as an effective...
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Nonlocal Meets Global: An Iterative Paradigm for Hyperspectral Image Restoration
Nonlocal lowrank tensor approximation has been developed as a stateof...
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Efficient, Simple and Automated Negative Sampling for Knowledge Graph Embedding
Negative sampling, which samples negative triplets from nonobserved one...
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Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering
Negative sampling approaches are prevalent in implicit collaborative fil...
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Simplifying Architecture Search for Graph Neural Network
Recent years have witnessed the popularity of Graph Neural Networks (GNN...
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Efficient LowRank Matrix Learning by Factorizable Nonconvex Regularization
Matrix learning is at the core of many machine learning problems. To enc...
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Generalizing Tensor Decomposition for Nary Relational Knowledge Bases
With the rapid development of knowledge bases (KBs), link prediction tas...
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Efficient Backbone Search for Scene Text Recognition
Scene text recognition (STR) is very challenging due to the diversity of...
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Neural Recurrent Structure Search for Knowledge Graph Embedding
Knowledge graph (KG) embedding is a fundamental problem in mining relati...
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Searching to Exploit Memorization Effect in Learning from Corrupted Labels
Sampleselection approaches, which attempt to pick up clean instances fr...
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Searching for Interaction Functions in Collaborative Filtering
Interaction function (IFC), which captures interactions among items and ...
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Differentiable Neural Architecture Search via Proximal Iterations
Neural architecture search (NAS) recently attracts much research attenti...
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Robust Learning from Noisy Sideinformation by Semidefinite Programming
Robustness recently becomes one of the major concerns among machine lear...
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AutoKGE: Searching Scoring Functions for Knowledge Graph Embedding
Knowledge graph embedding (KGE) aims to find low dimensional vector repr...
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Fewshot Learning: A Survey
The quest of `can machines think' and `can machines do what human do' ar...
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NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding
Knowledge Graph (KG) embedding is a fundamental problem in data mining r...
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Nonlocal Meets Global: An Integrated Paradigm for Hyperspectral Denoising
Nonlocal lowrank tensor approximation has been developed as a stateof...
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Privacypreserving Transfer Learning for Knowledge Sharing
In many practical machinelearning applications, it is critical to allow...
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Scalable Tensor Completion with Nonconvex Regularization
Lowrank tensor completion problem aims to recover a tensor from limited...
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Online Convolutional Sparse Coding with SampleDependent Dictionary
Convolutional sparse coding (CSC) has been popularly used for the learni...
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Cosampling: Training Robust Networks for Extremely Noisy Supervision
Training robust deep networks is challenging under noisy labels. Current...
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Millionaire: A Hintguided Approach for Crowdsourcing
Modern machine learning is migrating to the era of complex models, which...
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Learning with Heterogeneous Side Information Fusion for Recommender Systems
Recommender System (RS) is a hot area where artificial intelligence (AI)...
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Scalable Robust Matrix Factorization with Nonconvex Loss
Robust matrix factorization (RMF), which uses the ℓ_1loss, often outper...
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Efficient Robust Matrix Factorization with Nonconvex Loss
Robust matrix factorization (RMF), which uses the ℓ_1loss, often outper...
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Efficient Robust Matrix Factorization with Nonconvex Penalties
Robust matrix factorization (RMF) is a fundamental tool with lots of app...
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LargeScale LowRank Matrix Learning with Nonconvex Regularizers
Lowrank modeling has many important applications in computer vision and...
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Scalable Online Convolutional Sparse Coding
Convolutional sparse coding (CSC) improves sparse coding by learning a s...
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Accelerated and Inexact SoftImpute for LargeScale Matrix and Tensor Completion
Matrix and tensor completion aim to recover a lowrank matrix / tensor f...
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Lossaware Binarization of Deep Networks
Deep neural network models, though very powerful and highly successful, ...
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Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
The use of convex regularizers allows for easy optimization, though they...
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Fast LowRank Matrix Learning with Nonconvex Regularization
Lowrank modeling has a lot of important applications in machine learnin...
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Quanming Yao
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